The phrase "agricultural software" is correct and usable in written English.
It can be used when referring to software applications specifically designed for agricultural purposes, such as farm management, crop monitoring, or livestock tracking.
Example: "The new agricultural software has significantly improved our farm's productivity and efficiency."
Alternatives: "farming software" or "agribusiness software".
Harper's lab is developing an open-source agricultural hardware, software and data common with the goal of creating a more distributed, transparent and collaborative food system.
The Open Agriculture Initiative brings together partners from industry, government, and academia to develop an open source "food tech" research collective for the creation of the global agricultural hardware, software, and data commons.
Still, when it came to actually getting one for themselves, people tended to shy away from booths like AgStar, which manufacturers agricultural business software.
But it's interesting to see new players from agricultural-tech, software, and the civic-minded get involved in the food startup scene.
This approach, implemented in software called Agricultural Production Systems Simulator, consists of plug-in/pull-out modules and an infrastructure for inter-module communication.
Intel is also an equity investor in drone tech startups, including: Yuneec, which makes drones that automatically avoid obstacles even in tight spaces; Airware, developers of an operating system for commercial drones; and PrecisionHawk, makers of a fixed-wing drone and software for agricultural and other commercial drones.
Intel is also an equity investor in drone tech startups, including: Yuneec, which makes drones that automatically avoid obstacles even in tight spaces; Airware, developers of an operating system for commercial drones; and PrecisionHawk, makers of a fixed-wing drone and software for agricultural and other commercial drones.
Sections cover climate, agricultural, water resource, and ecosystem software tools.
Our department at the university emerged in 1968 as the focal point for software to handle agricultural data from the experimental stations at universities throughout the Southeast.
We identify three key issues to be taken into account when designing the next generation of software environments for agricultural modelling.
